Occluded Face Restoration Based on Generative Adversarial Networks

对抗制 面子(社会学概念) 计算机科学 生成语法 生成对抗网络 人工智能
作者
Mingming Zhang,Liang Huang,Maojing Zhu
出处
期刊:2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)
标识
DOI:10.1109/aemcse50948.2020.00074
摘要

In recent years, the combination of Convolutional Neural Networks and Generative Adversarial Networks has played a huge potential in the field of face restoration. In order to effectively repair the large area of random occlusion face, this paper constructs an improved Generative Adversarial Networks model based on the Context Encoder, and proposes a self-localization occlusion face image restoration algorithm. Firstly, the occluded part of the face is marked by occlusion locator, and then the marked face image is sent to the generator of Generative Adversarial Networks for restoration. The model generator uses the Convolutional Neural Networks of the Variational Autoencoder structure, and adds the Batch Normalization layer in the model to enhance the information prediction ability of the generator. At the same time, the discriminator is constructed by combining with VGG19, and the discriminator is trained against the generator. Through the experiment on CelebA face data set, this algorithm is significantly better than other methods in the aspect of random large area occlusion face image restoration.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
suger发布了新的文献求助10
刚刚
1秒前
干雅柏完成签到,获得积分10
2秒前
八九完成签到,获得积分10
3秒前
4秒前
干雅柏发布了新的文献求助10
5秒前
Stardust发布了新的文献求助10
5秒前
黑白和完成签到 ,获得积分10
6秒前
yang完成签到,获得积分10
7秒前
金蛋蛋发布了新的文献求助10
8秒前
量子星尘发布了新的文献求助10
10秒前
14秒前
19秒前
淡定的电源完成签到,获得积分10
22秒前
22秒前
lm发布了新的文献求助10
25秒前
27秒前
善学以致用应助孤独问旋采纳,获得10
27秒前
孙燕应助霸气安筠采纳,获得30
28秒前
李健应助科研通管家采纳,获得10
28秒前
汉堡包应助科研通管家采纳,获得10
28秒前
SYLH应助科研通管家采纳,获得20
28秒前
SYLH应助科研通管家采纳,获得10
28秒前
上官若男应助科研通管家采纳,获得10
28秒前
烟花应助科研通管家采纳,获得10
28秒前
丘比特应助科研通管家采纳,获得10
28秒前
SYLH应助科研通管家采纳,获得10
29秒前
CAOHOU应助科研通管家采纳,获得10
29秒前
SYLH应助科研通管家采纳,获得10
29秒前
CAOHOU应助科研通管家采纳,获得10
29秒前
SYLH应助科研通管家采纳,获得10
29秒前
科研通AI2S应助科研通管家采纳,获得10
29秒前
JamesPei应助科研通管家采纳,获得10
29秒前
ding应助科研通管家采纳,获得10
29秒前
29秒前
SYLH应助科研通管家采纳,获得10
29秒前
CAOHOU应助科研通管家采纳,获得10
29秒前
SYLH应助科研通管家采纳,获得20
29秒前
29秒前
31秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989378
求助须知:如何正确求助?哪些是违规求助? 3531442
关于积分的说明 11254002
捐赠科研通 3270126
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173